{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “AI Engineer – Must be Mandarin and English Fluent”, “description”: “
Position Overview
We are seeking an AI Engineer to join our Global Analytics team in London. This role focuses on the end-to-end lifecycle of production‑grade AI, from training and fine‑tuning specialized models to architecting high-performance inference pipelines.
We view AI as a rigorous engineering discipline. Beyond building models, you will write high-quality, maintainable Python code and ensure that every solution—whether a voice agent or a document processor—is built for reliability, low latency, and global scale.
Key Responsibilities
- Model Training & Fine‑Tuning: Lead the adaptation of Large Language Models (LLMs) for domain‑specific tasks using techniques like LoRA, QLoRA, and PEFT to balance performance with resource efficiency.
- Inference Optimization: Architect and optimize inference pipelines to minimize Time to First Token and maximize throughput, including quantization, caching strategies, and efficient batching.
- Production Engineering: Build and maintain real‑time AI pipelines using WebSockets and SSE, ensuring low‑latency delivery for voice (ASR/TTS) and text applications.
- Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability.
- Collaborative Delivery: Work closely with Business Analysts and internal stakeholders to bridge commercial requirements and technical implementation.
Qualifications
Technical Requirements
- Professional Experience: 5+ years in AI/ML engineering with a documented history of moving complex models from research into production.
- Python Mastery: Deep proficiency in Python, with a strong commitment to clean coding standards (SOLID/DRY), modular design, and comprehensive unit/integration testing.
- Generative AI Deep Dive: Hands‑on experience with LLM training cycles, parameter‑efficient fine‑tuning (PEFT), and sophisticated prompt engineering.
- Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton) and understanding of how to optimize models for GPU deployment.
- Infrastructure: Comfortable working in Linux‑based environments and proficient in managing containerized workloads and automated CI/CD pipelines.
- Advanced RAG: Experience building production‑ready Retrieval-Augmented Generation systems, including vector database management and semantic search optimization.
Preferred Qualifications
- Experience in the insurance or financial services sector.
- Deep knowledge of GPU architecture, CUDA, and hardware‑level performance optimization.
- Familiarity with Document Intelligence frameworks (OCR, layout analysis, and multimodal extraction).
- MUST be fluent in Mandarin.
Company: Chubb
Location: London
Job Description:
Position Overview
We are seeking an AI Engineer to join our Global Analytics team in London. This role focuses on the end-to-end lifecycle of production‑grade AI, from training and fine‑tuning specialized models to architecting high-performance inference pipelines.
We view AI as a rigorous engineering discipline. Beyond building models, you will write high-quality, maintainable Python code and ensure that every solution—whether a voice agent or a document processor—is built for reliability, low latency, and global scale.
Key Responsibilities
- Model Training & Fine‑Tuning: Lead the adaptation of Large Language Models (LLMs) for domain‑specific tasks using techniques like LoRA, QLoRA, and PEFT to balance performance with resource efficiency.
- Inference Optimization: Architect and optimize inference pipelines to minimize Time to First Token and maximize throughput, including quantization, caching strategies, and efficient batching.
- Production Engineering: Build and maintain real‑time AI pipelines using WebSockets and SSE, ensuring low‑latency delivery for voice (ASR/TTS) and text applications.
- Architecture & MLOps: Deploy and orchestrate models within containerized microservice architectures (Docker/Kubernetes), ensuring robust monitoring, security, and scalability.
- Collaborative Delivery: Work closely with Business Analysts and internal stakeholders to bridge commercial requirements and technical implementation.
Qualifications
Technical Requirements
- Professional Experience: 5+ years in AI/ML engineering with a documented history of moving complex models from research into production.
- Python Mastery: Deep proficiency in Python, with a strong commitment to clean coding standards (SOLID/DRY), modular design, and comprehensive unit/integration testing.
- Generative AI Deep Dive: Hands‑on experience with LLM training cycles, parameter‑efficient fine‑tuning (PEFT), and sophisticated prompt engineering.
- Inference Stack: Experience with high-performance inference servers (e.g., vLLM, TGI, or Triton) and understanding of how to optimize models for GPU deployment.
- Infrastructure: Comfortable working in Linux‑based environments and proficient in managing containerized workloads and automated CI/CD pipelines.
- Advanced RAG: Experience building production‑ready Retrieval-Augmented Generation systems, including vector database management and semantic search optimization.
Preferred Qualifications
- Experience in the insurance or financial services sector.
- Deep knowledge of GPU architecture, CUDA, and hardware‑level performance optimization.
- Familiarity with Document Intelligence frameworks (OCR, layout analysis, and multimodal extraction).
- MUST be fluent in Mandarin.
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Posted: May 9th, 2026